Checkup2Action is a new multimodal dataset and benchmark for generating safe, prioritized action cards from real-world clinical check-up reports using large language models.
PubMedQA: A Dataset for Biomedical Research Question Answering
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
A graphlet-anchored framework generates 119,856 factually grounded biomedical QA pairs that improve accuracy on PubMedQA and MedQA benchmarks.
PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
EvidenceNet releases disease-specific biomedical knowledge bases with 7,872 and 6,622 evidence records for HCC and CRC, plus graphs, extracted via LLM pipeline with reported high fidelity.
A shared metaprompt distilled from 21 clinical tasks enables adaptation to 10 held-out datasets across five task types with under 0.05% parameters, outperforming LoRA by 1.5-1.7% and single-task prompt tuning by 6.1-6.6%.
citing papers explorer
-
Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation
Checkup2Action is a new multimodal dataset and benchmark for generating safe, prioritized action cards from real-world clinical check-up reports using large language models.
-
BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets
A graphlet-anchored framework generates 119,856 factually grounded biomedical QA pairs that improve accuracy on PubMedQA and MedQA benchmarks.
-
PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs
PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
-
M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
M3-Embedding is a single model for multi-lingual, multi-functional, and multi-granular text embeddings trained via self-knowledge distillation that achieves new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.
-
Compared to What? Baselines and Metrics for Counterfactual Prompting
Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
-
Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning
EvidenceNet releases disease-specific biomedical knowledge bases with 7,872 and 6,622 evidence records for HCC and CRC, plus graphs, extracted via LLM pipeline with reported high fidelity.
-
A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP
A shared metaprompt distilled from 21 clinical tasks enables adaptation to 10 held-out datasets across five task types with under 0.05% parameters, outperforming LoRA by 1.5-1.7% and single-task prompt tuning by 6.1-6.6%.